In many practical, clinical and industrial, applications it is often requested the evaluation of properties in correspondence of a specific element of a system and the quantification of the variation of such properties with time. The application that stimulated the development of the present method derives from capability of tracking the moving vascular wall (e.g. myocardium) from cardiovascular images in such a way to extract from the image the properties (velocity, brightness, etc.) evaluated in correspondence of the tissue.
One driving example is the analysis of the time-course of blood flow in the capillary bed of the myocardium, based on the echo-contrast imaging. The health of the myocardium can be established, among other ways, from the ability of the blood to reach all muscular cells; if a myocardial region is not perfused then derives mechanical failure (e.g. during angina or myocardial infarction). It is therefore important to develop techniques able to evaluate the perfusion properties of the different tissue regions. The quantification of myocardial perfusion is made by introducing a contrast agent in vein, it moves with the blood and quantification of its presence in the myocardial tissue is equivalent to quantification of myocardial perfusion (Becher and Burns 2000). The analysis is made utilizing the ability of ultrasound machine to detect the echo enhancement deriving from the contrast medium that perfuses the myocardium. Recent examples of quantitative analysis of segmental perfusion are reported in literature (Nor-Avi et al. 1993; Wei et al. 1998; Masugata et al. 2001).
Crucial for an adequate quantification of the contrast signal is the ability to follow the systolic and diastolic movement of the heart walls. With the respect of the ultrasound probe, the heart show not only inherent movement but also displacements due to respiration. Moreover, the physician performing the examination can move the probe itself during the acquisition of the data. For these reasons, if we try to register the signal of the wall utilizing a region of interest (ROI) placed at a fixed location, frequently, the ROI fall on other structures (like left or the right ventricular cavities or outside the heart). For these reasons, only if the wall is continuously tracked we can extract the signal originating from the tissue and not outside of it and so extract quantitative parameters of regional perfusion.
Such an approach has a widespread application not only in echocardiography (e.g perfusion study analysis, regional wall velocity analysis and quantification, computation of segmental strain and strain rate (Heimdal et al. 1998; Voigt et al. 2000)) but also in industrial applications when the tracking of a moving material is necessary and in some applications of visual recognition by intelligent electronic devices.
Currently, the quantification of wall-related properties is performed simply by analyzing the properties within a ROI, (sometimes just a few pixels within the image) selected well inside the myocardial tissue. It is then important to verify that the selected ROI remains inside the tissue in all the images of the sequence otherwise information that do not pertain to the tissue are included and the analysis is corrupted. To make sure that we aren't introducing erroneous samples in the dataset, the sequence should be reviewed frame by frame: when the ROI falls outside the tissue it must be moved manually on the wall. It is evident how such an approach is inherently extremely time-consuming (in most case for each ROI we must review more than 100 frames and a compete evaluation requires to analyze up to 20 different ROI). Sometimes this procedure can performed automatically with methods that depend from the software available. In most cases, these are based on standard edge detection algorithm or on cross-correlation alignment methods (Pratt 1991), however these technique do not guarantee the accuracy of the results that must be still verified manually because they incorporate no information about the structure and the geometry, of the object that must be recognized.
We present here a novel method that allows to continuously tracks in time the wall contained inside a two or three-dimensional representation (images) that is well suited for the case when the wall is relatively thin. After the wall is recognized it is straightforward to analyze the time evolution of properties in correspondence of the detected wall.